The University of Adelaide, School of Computer Science

The University of Adelaide, School of Computer Science
Computer Architecture A Quantitative Approach, Sixth Edition The University of Adelaide, School of Computer Science 8 November 2018 Chapter 7 Domain-Specific Architectures Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 Introduction Introduction Moore’s Law enabled: Deep memory hierarchy Wide SIMD units Deep pipelines Branch prediction Out-of-order execution Speculative prefetching Multithreading Multiprocessing Objective: Extract performance from software that is oblivious to architecture Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 Introduction Introduction Need factor of 100 improvements in number of operations per instruction Requires domain specific architectures For ASICs, NRE cannot be amoratized over large volumes FPGAs are less efficient than ASICs Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 Guidelines for DSAs Guidelines for DSAs Use dedicated memories to minimize data movement Invest resources into more arithmetic units or bigger memories Use the easiest form of parallelism that matches the domain Reduce data size and type to the simplest needed for the domain Use a domain-specific programming language Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Example: Deep Neural Networks
The University of Adelaide, School of Computer Science 8 November 2018 Example: Deep Neural Networks Inpired by neuron of the brain Computes non-linear “activiation” function of the weighted sum of input values Neurons arranged in layers Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Example: Deep Neural Networks
The University of Adelaide, School of Computer Science 8 November 2018 Example: Deep Neural Networks Most practioners will choose an existing design Topology Data type Training (learning): Calculate weights using backpropagation algorithm Supervised learning: stocastic graduate descent Inferrence: use neural network for classification Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Multi-Layer Perceptrons
The University of Adelaide, School of Computer Science 8 November 2018 Multi-Layer Perceptrons Parameters: Dim[i]: number of neurons Dim[i-1]: dimension of input vector Number of weights: Dim[i-1] x Dim[i] Operations: 2 x Dim[i-1] x Dim[i] Operations/weight: 2 Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Convolutional Neural Network
The University of Adelaide, School of Computer Science 8 November 2018 Convolutional Neural Network Computer vision Each layer raises the level of abstraction First layer recognizes horizontal and vertical lines Second layer recognizes corners Third layer recognizes shapes Fourth layer recognizes features, such as ears of a dog Higher layers recognizes different breeds of dogs Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

The University of Adelaide, School of Computer Science 8 November 2018 Convolutional Neural Network Parameters: DimFM[i-1]: Dimension of the (square) input Feature Map DimFM[i]: Dimension of the (square) output Feature Map DimSten[i]: Dimension of the (square) stencil NumFM[i-1]: Number of input Feature Maps NumFM[i]: Number of output Feature Maps Number of neurons: NumFM[i] x DimFM[i]2 Number of weights per output Feature Map: NumFM[i-1] x DimSten[i]2 Total number of weights per layer: NumFM[i] x Number of weights per output Feature Map Number of operations per output Feature Map: 2 x DimFM[i]2 x Number of weights per output Feature Map Total number of operations per layer: NumFM[i] x Number of operations per output Feature Map = 2 x DimFM[i]2 x NumFM[i] x Number of weights per output Feature Map = 2 x DimFM[i]2 x Total number of weights per layer Operations/Weight: 2 x DimFM[i]2 Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Recurrent Neural Network
The University of Adelaide, School of Computer Science 8 November 2018 Recurrent Neural Network Speech recognition and language translation Long short-term memory (LSTM) network Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Recurrent Neural Network
The University of Adelaide, School of Computer Science 8 November 2018 Recurrent Neural Network Parameters: Number of weights per cell: 3 x (3 x Dim x Dim)+(2 x Dim x Dim) + (1 x Dim x Dim) = 12 x Dim2 Number of operations for the 5 vector-matrix multiplies per cell: 2 x Number of weights per cell = 24 x Dim2 Number of operations for the 3 element-wise multiplies and 1 addition (vectors are all the size of the output): 4 x Dim Total number of operations per cell (5 vector-matrix multiplies and the 4 element-wise operations): 24 x Dim2 + 4 x Dim Operations/Weight: ~2 Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

The University of Adelaide, School of Computer Science 8 November 2018 Convolutional Neural Network Batches: Reuse weights once fetched from memory across multiple inputs Increases operational intensity Quantization Use 8- or 16-bit fixed point Summary: Need the following kernels: Matrix-vector multiply Matrix-matrix multiply Stencil ReLU Sigmoid Hyperbolic tangeant Example: Deep Neural Networks Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Tensor Processing Unit
The University of Adelaide, School of Computer Science 8 November 2018 Tensor Processing Unit Google’s DNN ASIC 256 x bit matrix multiply unit Large software-managed scratchpad Coprocessor on the PCIe bus Tensor Processing Unit Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Tensor Processing Unit
The University of Adelaide, School of Computer Science 8 November 2018 Tensor Processing Unit Tensor Processing Unit Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 TPU ISA Read_Host_Memory Reads memory from the CPU memory into the unified buffer Read_Weights Reads weights from the Weight Memory into the Weight FIFO as input to the Matrix Unit MatrixMatrixMultiply/Convolve Perform a matrix-matrix multiply, a vector-matrix multiply, an element-wise matrix multiply, an element-wise vector multiply, or a convolution from the Unified Buffer into the accumulators takes a variable-sized B*256 input, multiplies it by a 256x256 constant input, and produces a B*256 output, taking B pipelined cycles to complete Activate Computes activation function Write_Host_Memory Writes data from unified buffer into host memory Tensor Processing Unit Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

The TPU and the Guidelines
The University of Adelaide, School of Computer Science 8 November 2018 The TPU and the Guidelines Use dedicated memories 24 MiB dedicated buffer, 4 MiB accumulator buffers Invest resources in arithmetic units and dedicated memories 60% of the memory and 250X the arithmetic units of a server-class CPU Use the easiest form of parallelism that matches the domain Exploits 2D SIMD parallelism Reduce the data size and type needed for the domain Primarily uses 8-bit integers Use a domain-specific programming language Uses TensorFlow Tensor Processing Unit Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 Microsoft Catapult Microsoft Capapult Needed to be general purpose and power efficient Uses FPGA PCIe board with dedicated 20 Gbps network in 6 x 8 torus Each of the 48 servers in half the rack has a Catapult board Limited to 25 watts 32 MiB Flash memory Two banks of DDR (11 GB/s) and 8 GiB DRAM FPGA (unconfigured) has bit ALUs and 5 MiB of on-chip memory Programmed in Verilog RTL Shell is 23% of the FPGA Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Microsoft Catapult: CNN
The University of Adelaide, School of Computer Science 8 November 2018 Microsoft Catapult: CNN Microsoft Capapult CNN accelerator, mapped across multiple FPGAs Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Microsoft Catapult: CNN
The University of Adelaide, School of Computer Science 8 November 2018 Microsoft Catapult: CNN Microsoft Capapult Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Microsoft Catapult: Search Ranking
The University of Adelaide, School of Computer Science 8 November 2018 Microsoft Catapult: Search Ranking Microsoft Capapult Feature extraction (1 FPGA) Extracts 4500 features for every document-query pair, e.g. frequency in which the query appears in the page Systolic array of FSMs Free-form expressions (2 FPGAs) Calculates feature combinations Machine-learned Scoring (1 FPGA for compression, 3 FPGAs calculate score) Uses results of previous two stages to calculate floating-point score One FPGA allocated as a hot-spare Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

The University of Adelaide, School of Computer Science 8 November 2018 Microsoft Catapult: Search Ranking Microsoft Capapult Free-form expression evaluation 60 core processor Pipelined cores Each core supports four threads that can hide each other’s latency Threads are statically prioritized according to thread latency Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

The University of Adelaide, School of Computer Science 8 November 2018 Microsoft Catapult: Search Ranking Microsoft Capapult Version 2 of Catapult Placed the FPGA between the CPU and NIC Increased network from 10 Gb/s to 40 Gb/s Also performs network acceleration Shell now consumes 44% of the FPGA Now FPGA performs only feature extraction Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Catapult and the Guidelines
The University of Adelaide, School of Computer Science 8 November 2018 Catapult and the Guidelines Microsoft Capapult Use dedicated memories 5 MiB dedicated memory Invest resources in arithmetic units and dedicated memories 3926 ALUs Use the easiest form of parallelism that matches the domain 2D SIMD for CNN, MISD parallelism for search scoring Reduce the data size and type needed for the domain Uses mixture of 8-bit integers and 64-bit floating-point Use a domain-specific programming language Uses Verilog RTL; Microsoft did not follow this guideline Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 Intel Crest Intel Crest DNN training 16-bit fixed point Operates on blocks of 32x32 matrices SRAM + HBM2 Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 Pixel Visual Core Pixel Visual Core Pixel Visual Core Image Processing Unit Performs stencil operations Decended from Image Signal processor Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

8 November 2018 Pixel Visual Core Pixel Visual Core Software written in Halide, a DSL Compiled to virtual ISA vISA is lowered to physical ISA using application-specific parameters pISA is VLSI Optimized for energy Power Budget is 6 to 8 W for bursts of seconds, dropping to tens of milliwatts when not in use 8-bit DRAM access equivalent energy as 12,500 8-bit integer operations or 7 to bit SRAM accesses IEEE 754 operations require 22X to 150X of the cost of 8-bit integer operations Optimized for 2D access 2D SIMD unit On-chip SRAM structured using a square geometry Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Visual Core and the Guidelines
The University of Adelaide, School of Computer Science 8 November 2018 Visual Core and the Guidelines Microsoft Capapult Use dedicated memories MiB dedicated memory per core Invest resources in arithmetic units and dedicated memories 16x16 2D array of processing elements per core and 2D shifting network per core Use the easiest form of parallelism that matches the domain 2D SIMD and VLIW Reduce the data size and type needed for the domain Uses mixture of 8-bit and 16-bit integers Use a domain-specific programming language Halide for image processing and TensorFlow for CNNs Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

Fallacies and Pitfalls
The University of Adelaide, School of Computer Science 8 November 2018 Fallacies and Pitfalls Microsoft Capapult It costs $100 million to design a custom chip Performance counters added as an afterthought Architects are tackling the right DNN tasks For DNN hardware, inferences per second (IPS) is a fair summary performance metric Being ignorant of architecture history when designing an DSA Copyright © 2019, Elsevier Inc. All rights Reserved Chapter 2 — Instructions: Language of the Computer

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